Overview

Dataset statistics

Number of variables17
Number of observations21641
Missing cells25872
Missing cells (%)7.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 MiB
Average record size in memory136.0 B

Variable types

TimeSeries4
Categorical2
Numeric10
DateTime1

Alerts

Week is highly overall correlated with MonthHigh correlation
Year is highly overall correlated with SiteNumberHigh correlation
Month is highly overall correlated with WeekHigh correlation
SiteNumber is highly overall correlated with YearHigh correlation
Moisture is highly overall correlated with TextureHigh correlation
Texture is highly overall correlated with MoistureHigh correlation
Tiny is highly overall correlated with SmallHigh correlation
Small is highly overall correlated with Tiny and 1 other fieldsHigh correlation
Large is highly overall correlated with Small and 1 other fieldsHigh correlation
Mature is highly overall correlated with LargeHigh correlation
SiteLetter is highly overall correlated with RegionHigh correlation
Region is highly overall correlated with SiteLetterHigh correlation
Moisture has 14441 (66.7%) missing valuesMissing
Texture has 2279 (10.5%) missing valuesMissing
Canopy has 2277 (10.5%) missing valuesMissing
Maintenance has 2277 (10.5%) missing valuesMissing
Flowers has 2278 (10.5%) missing valuesMissing
Leaf flushing has 2316 (10.7%) missing valuesMissing
Week is non stationaryNon stationary
Year is non stationaryNon stationary
Month is non stationaryNon stationary
SiteNumber is non stationaryNon stationary
Week is seasonalSeasonal
Year is seasonalSeasonal
Month is seasonalSeasonal
SiteNumber is seasonalSeasonal
Maintenance has 785 (3.6%) zerosZeros
Flowers has 1723 (8.0%) zerosZeros
Leaf flushing has 1716 (7.9%) zerosZeros
Tiny has 2066 (9.5%) zerosZeros
Small has 1686 (7.8%) zerosZeros
Large has 1937 (9.0%) zerosZeros
Mature has 1477 (6.8%) zerosZeros

Reproduction

Analysis started2023-07-30 12:19:54.062630
Analysis finished2023-07-30 12:20:19.394266
Duration25.33 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Week
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct33
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.886835
Minimum2
Maximum51
Zeros0
Zeros (%)0.0%
Memory size169.2 KiB
2023-07-30T14:20:19.451435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q114
median27
Q339
95-th percentile50
Maximum51
Range49
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.467817
Coefficient of variation (CV)0.53810042
Kurtosis-1.1360315
Mean26.886835
Median Absolute Deviation (MAD)12
Skewness-0.038881877
Sum581858
Variance209.31774
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.249472609 × 10-13
2023-07-30T14:20:19.576898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
39 1836
 
8.5%
30 1827
 
8.4%
12 1727
 
8.0%
34 1722
 
8.0%
50 1638
 
7.6%
43 1569
 
7.3%
3 1560
 
7.2%
21 1503
 
6.9%
8 1408
 
6.5%
17 1153
 
5.3%
Other values (23) 5698
26.3%
ValueCountFrequency (%)
2 68
 
0.3%
3 1560
7.2%
4 258
 
1.2%
5 36
 
0.2%
7 104
 
0.5%
8 1408
6.5%
12 1727
8.0%
13 199
 
0.9%
14 92
 
0.4%
16 320
 
1.5%
ValueCountFrequency (%)
51 225
 
1.0%
50 1638
7.6%
47 494
 
2.3%
46 324
 
1.5%
45 49
 
0.2%
44 125
 
0.6%
43 1569
7.3%
42 167
 
0.8%
39 1836
8.5%
38 159
 
0.7%
2023-07-30T14:20:20.126574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ACF and PACF

Year
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.8183
Minimum1997
Maximum2023
Zeros0
Zeros (%)0.0%
Memory size169.2 KiB
2023-07-30T14:20:20.320891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile2001
Q12009
median2015
Q32019
95-th percentile2022
Maximum2023
Range26
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.6598626
Coefficient of variation (CV)0.0033070822
Kurtosis-0.58234014
Mean2013.8183
Median Absolute Deviation (MAD)5
Skewness-0.62053207
Sum43581042
Variance44.35377
MonotonicityIncreasing
Augmented Dickey-Fuller test p-value0.01449242644
2023-07-30T14:20:20.416945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
2021 1506
 
7.0%
2022 1437
 
6.6%
2019 1431
 
6.6%
2020 1369
 
6.3%
2018 1366
 
6.3%
2016 1245
 
5.8%
2017 1234
 
5.7%
2013 1133
 
5.2%
2014 1093
 
5.1%
2015 1091
 
5.0%
Other values (17) 8736
40.4%
ValueCountFrequency (%)
1997 35
 
0.2%
1998 284
1.3%
1999 249
1.2%
2000 387
1.8%
2001 464
2.1%
2002 358
1.7%
2003 297
1.4%
2004 522
2.4%
2005 548
2.5%
2006 437
2.0%
ValueCountFrequency (%)
2023 837
3.9%
2022 1437
6.6%
2021 1506
7.0%
2020 1369
6.3%
2019 1431
6.6%
2018 1366
6.3%
2017 1234
5.7%
2016 1245
5.8%
2015 1091
5.0%
2014 1093
5.1%
2023-07-30T14:20:21.012769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ACF and PACF

Month
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3759531
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size169.2 KiB
2023-07-30T14:20:21.215244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.337786
Coefficient of variation (CV)0.52349601
Kurtosis-1.0818013
Mean6.3759531
Median Absolute Deviation (MAD)3
Skewness0.024768852
Sum137982
Variance11.140815
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.742371346 × 10-13
2023-07-30T14:20:21.312518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 2121
9.8%
7 2051
9.5%
5 2022
9.3%
3 2018
9.3%
9 1995
9.2%
8 1936
8.9%
1 1922
8.9%
10 1910
8.8%
12 1863
8.6%
2 1512
7.0%
Other values (2) 2291
10.6%
ValueCountFrequency (%)
1 1922
8.9%
2 1512
7.0%
3 2018
9.3%
4 1473
6.8%
5 2022
9.3%
6 2121
9.8%
7 2051
9.5%
8 1936
8.9%
9 1995
9.2%
10 1910
8.8%
ValueCountFrequency (%)
12 1863
8.6%
11 818
 
3.8%
10 1910
8.8%
9 1995
9.2%
8 1936
8.9%
7 2051
9.5%
6 2121
9.8%
5 2022
9.3%
4 1473
6.8%
3 2018
9.3%
2023-07-30T14:20:21.841938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ACF and PACF

SiteLetter
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.2 KiB
SP
6440 
GA
3509 
MA
3314 
DA
3143 
AG
2509 
Other values (3)
2726 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters43282
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAG
2nd rowSP
3rd rowSP
4th rowSP
5th rowSP

Common Values

ValueCountFrequency (%)
SP 6440
29.8%
GA 3509
16.2%
MA 3314
15.3%
DA 3143
14.5%
AG 2509
 
11.6%
FR 2455
 
11.3%
DL 158
 
0.7%
VA 113
 
0.5%

Length

2023-07-30T14:20:22.028463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T14:20:22.150856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
sp 6440
29.8%
ga 3509
16.2%
ma 3314
15.3%
da 3143
14.5%
ag 2509
 
11.6%
fr 2455
 
11.3%
dl 158
 
0.7%
va 113
 
0.5%

Most occurring characters

ValueCountFrequency (%)
A 12588
29.1%
S 6440
14.9%
P 6440
14.9%
G 6018
13.9%
M 3314
 
7.7%
D 3301
 
7.6%
F 2455
 
5.7%
R 2455
 
5.7%
L 158
 
0.4%
V 113
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 43282
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 12588
29.1%
S 6440
14.9%
P 6440
14.9%
G 6018
13.9%
M 3314
 
7.7%
D 3301
 
7.6%
F 2455
 
5.7%
R 2455
 
5.7%
L 158
 
0.4%
V 113
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 43282
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 12588
29.1%
S 6440
14.9%
P 6440
14.9%
G 6018
13.9%
M 3314
 
7.7%
D 3301
 
7.6%
F 2455
 
5.7%
R 2455
 
5.7%
L 158
 
0.4%
V 113
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43282
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 12588
29.1%
S 6440
14.9%
P 6440
14.9%
G 6018
13.9%
M 3314
 
7.7%
D 3301
 
7.6%
F 2455
 
5.7%
R 2455
 
5.7%
L 158
 
0.4%
V 113
 
0.3%

SiteNumber
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct153
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.18192
Minimum1
Maximum523
Zeros0
Zeros (%)0.0%
Memory size169.2 KiB
2023-07-30T14:20:22.257292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median24
Q3134
95-th percentile416
Maximum523
Range522
Interquartile range (IQR)124

Descriptive statistics

Standard deviation141.49486
Coefficient of variation (CV)1.3079344
Kurtosis0.11228814
Mean108.18192
Median Absolute Deviation (MAD)20
Skewness1.2568643
Sum2341165
Variance20020.796
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.115189964 × 10-7
2023-07-30T14:20:22.386622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 885
 
4.1%
10 801
 
3.7%
11 639
 
3.0%
5 611
 
2.8%
23 595
 
2.7%
2 581
 
2.7%
1 568
 
2.6%
13 541
 
2.5%
20 540
 
2.5%
3 534
 
2.5%
Other values (143) 15346
70.9%
ValueCountFrequency (%)
1 568
2.6%
2 581
2.7%
3 534
2.5%
4 885
4.1%
5 611
2.8%
6 219
 
1.0%
7 241
 
1.1%
8 445
2.1%
9 527
2.4%
10 801
3.7%
ValueCountFrequency (%)
523 24
 
0.1%
522 15
 
0.1%
521 24
 
0.1%
519 24
 
0.1%
517 2
 
< 0.1%
504 25
0.1%
503 25
0.1%
429 4
 
< 0.1%
428 28
0.1%
427 61
0.3%
2023-07-30T14:20:22.942204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ACF and PACF

Moisture
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct546
Distinct (%)7.6%
Missing14441
Missing (%)66.7%
Infinite0
Infinite (%)0.0%
Mean18.519069
Minimum0
Maximum310
Zeros126
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size169.2 KiB
2023-07-30T14:20:23.144765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.3
Q111
median17.3
Q323.9
95-th percentile38.2
Maximum310
Range310
Interquartile range (IQR)12.9

Descriptive statistics

Standard deviation11.32764
Coefficient of variation (CV)0.61167438
Kurtosis78.902106
Mean18.519069
Median Absolute Deviation (MAD)6.4
Skewness4.1157402
Sum133337.3
Variance128.31544
MonotonicityNot monotonic
2023-07-30T14:20:23.257949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 126
 
0.6%
11.6 51
 
0.2%
16.7 50
 
0.2%
9.5 49
 
0.2%
15.6 49
 
0.2%
20.3 48
 
0.2%
18.1 48
 
0.2%
18.5 47
 
0.2%
12.3 47
 
0.2%
17.4 47
 
0.2%
Other values (536) 6638
30.7%
(Missing) 14441
66.7%
ValueCountFrequency (%)
0 126
0.6%
0.2 1
 
< 0.1%
0.4 1
 
< 0.1%
0.9 2
 
< 0.1%
1 4
 
< 0.1%
1.1 1
 
< 0.1%
1.2 1
 
< 0.1%
1.3 2
 
< 0.1%
1.5 5
 
< 0.1%
1.7 1
 
< 0.1%
ValueCountFrequency (%)
310 1
< 0.1%
230 1
< 0.1%
115.6 1
< 0.1%
68.5 1
< 0.1%
68.2 2
< 0.1%
67.6 1
< 0.1%
67.4 1
< 0.1%
66.2 1
< 0.1%
64.7 1
< 0.1%
63.4 1
< 0.1%

Texture
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing2279
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean2.7409875
Minimum0
Maximum9
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size169.2 KiB
2023-07-30T14:20:23.355320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.75608689
Coefficient of variation (CV)0.27584471
Kurtosis0.35310103
Mean2.7409875
Median Absolute Deviation (MAD)0
Skewness-0.28565902
Sum53071
Variance0.57166739
MonotonicityNot monotonic
2023-07-30T14:20:23.420508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 10581
48.9%
2 5255
24.3%
4 2358
 
10.9%
1 1102
 
5.1%
5 55
 
0.3%
0 10
 
< 0.1%
9 1
 
< 0.1%
(Missing) 2279
 
10.5%
ValueCountFrequency (%)
0 10
 
< 0.1%
1 1102
 
5.1%
2 5255
24.3%
3 10581
48.9%
4 2358
 
10.9%
5 55
 
0.3%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
5 55
 
0.3%
4 2358
 
10.9%
3 10581
48.9%
2 5255
24.3%
1 1102
 
5.1%
0 10
 
< 0.1%

Canopy
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)< 0.1%
Missing2277
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean2.8176513
Minimum0
Maximum5
Zeros69
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size169.2 KiB
2023-07-30T14:20:23.501718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.77470757
Coefficient of variation (CV)0.27494799
Kurtosis0.1433019
Mean2.8176513
Median Absolute Deviation (MAD)0
Skewness-0.35350844
Sum54561
Variance0.60017181
MonotonicityNot monotonic
2023-07-30T14:20:23.584445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 9988
46.2%
2 5181
23.9%
4 3347
 
15.5%
1 762
 
3.5%
0 69
 
0.3%
5 17
 
0.1%
(Missing) 2277
 
10.5%
ValueCountFrequency (%)
0 69
 
0.3%
1 762
 
3.5%
2 5181
23.9%
3 9988
46.2%
4 3347
 
15.5%
5 17
 
0.1%
ValueCountFrequency (%)
5 17
 
0.1%
4 3347
 
15.5%
3 9988
46.2%
2 5181
23.9%
1 762
 
3.5%
0 69
 
0.3%

Maintenance
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing2277
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean2.5586656
Minimum0
Maximum31
Zeros785
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size169.2 KiB
2023-07-30T14:20:23.665761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum31
Range31
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0284482
Coefficient of variation (CV)0.4019471
Kurtosis29.994935
Mean2.5586656
Median Absolute Deviation (MAD)1
Skewness0.57645529
Sum49546
Variance1.0577057
MonotonicityNot monotonic
2023-07-30T14:20:23.740351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 7956
36.8%
2 5594
25.8%
4 3101
 
14.3%
1 1895
 
8.8%
0 785
 
3.6%
5 32
 
0.1%
31 1
 
< 0.1%
(Missing) 2277
 
10.5%
ValueCountFrequency (%)
0 785
 
3.6%
1 1895
 
8.8%
2 5594
25.8%
3 7956
36.8%
4 3101
 
14.3%
5 32
 
0.1%
31 1
 
< 0.1%
ValueCountFrequency (%)
31 1
 
< 0.1%
5 32
 
0.1%
4 3101
 
14.3%
3 7956
36.8%
2 5594
25.8%
1 1895
 
8.8%
0 785
 
3.6%

Flowers
Real number (ℝ)

MISSING  ZEROS 

Distinct10
Distinct (%)0.1%
Missing2278
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean1.966689
Minimum0
Maximum22
Zeros1723
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size169.2 KiB
2023-07-30T14:20:23.820351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum22
Range22
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.06826
Coefficient of variation (CV)0.54317685
Kurtosis8.5035016
Mean1.966689
Median Absolute Deviation (MAD)1
Skewness0.55462094
Sum38081
Variance1.1411793
MonotonicityNot monotonic
2023-07-30T14:20:23.901619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 7316
33.8%
3 4607
21.3%
1 4447
20.5%
0 1723
 
8.0%
4 1218
 
5.6%
5 46
 
0.2%
9 2
 
< 0.1%
11 2
 
< 0.1%
22 1
 
< 0.1%
17 1
 
< 0.1%
(Missing) 2278
 
10.5%
ValueCountFrequency (%)
0 1723
 
8.0%
1 4447
20.5%
2 7316
33.8%
3 4607
21.3%
4 1218
 
5.6%
5 46
 
0.2%
9 2
 
< 0.1%
11 2
 
< 0.1%
17 1
 
< 0.1%
22 1
 
< 0.1%
ValueCountFrequency (%)
22 1
 
< 0.1%
17 1
 
< 0.1%
11 2
 
< 0.1%
9 2
 
< 0.1%
5 46
 
0.2%
4 1218
 
5.6%
3 4607
21.3%
2 7316
33.8%
1 4447
20.5%
0 1723
 
8.0%

Leaf flushing
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing2316
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean1.9954463
Minimum0
Maximum11
Zeros1716
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size169.2 KiB
2023-07-30T14:20:23.982894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0682951
Coefficient of variation (CV)0.53536648
Kurtosis-0.31463815
Mean1.9954463
Median Absolute Deviation (MAD)1
Skewness-0.030934029
Sum38562
Variance1.1412544
MonotonicityNot monotonic
2023-07-30T14:20:24.056288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 6616
30.6%
3 5205
24.1%
1 4498
20.8%
0 1716
 
7.9%
4 1239
 
5.7%
5 50
 
0.2%
11 1
 
< 0.1%
(Missing) 2316
 
10.7%
ValueCountFrequency (%)
0 1716
 
7.9%
1 4498
20.8%
2 6616
30.6%
3 5205
24.1%
4 1239
 
5.7%
5 50
 
0.2%
11 1
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
5 50
 
0.2%
4 1239
 
5.7%
3 5205
24.1%
2 6616
30.6%
1 4498
20.8%
0 1716
 
7.9%

Tiny
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct235
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.127628
Minimum0
Maximum715
Zeros2066
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size169.2 KiB
2023-07-30T14:20:24.153481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10
Q323
95-th percentile64
Maximum715
Range715
Interquartile range (IQR)20

Descriptive statistics

Standard deviation27.087438
Coefficient of variation (CV)1.4942626
Kurtosis56.229757
Mean18.127628
Median Absolute Deviation (MAD)8
Skewness5.1686468
Sum392300
Variance733.72927
MonotonicityNot monotonic
2023-07-30T14:20:24.266852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2066
 
9.5%
1 1289
 
6.0%
2 1249
 
5.8%
3 1107
 
5.1%
4 1039
 
4.8%
5 967
 
4.5%
6 935
 
4.3%
7 762
 
3.5%
8 740
 
3.4%
9 661
 
3.1%
Other values (225) 10826
50.0%
ValueCountFrequency (%)
0 2066
9.5%
1 1289
6.0%
2 1249
5.8%
3 1107
5.1%
4 1039
4.8%
5 967
4.5%
6 935
4.3%
7 762
 
3.5%
8 740
 
3.4%
9 661
 
3.1%
ValueCountFrequency (%)
715 1
< 0.1%
480 1
< 0.1%
440 1
< 0.1%
436 1
< 0.1%
416 2
< 0.1%
403 1
< 0.1%
387 1
< 0.1%
374 1
< 0.1%
360 1
< 0.1%
341 1
< 0.1%

Small
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct217
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.567026
Minimum0
Maximum509
Zeros1686
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size169.2 KiB
2023-07-30T14:20:24.387977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median11
Q324
95-th percentile62
Maximum509
Range509
Interquartile range (IQR)20

Descriptive statistics

Standard deviation24.775041
Coefficient of variation (CV)1.334357
Kurtosis39.883807
Mean18.567026
Median Absolute Deviation (MAD)8
Skewness4.3503577
Sum401809
Variance613.80264
MonotonicityNot monotonic
2023-07-30T14:20:24.494761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1686
 
7.8%
1 1184
 
5.5%
2 1104
 
5.1%
3 1048
 
4.8%
4 977
 
4.5%
5 926
 
4.3%
6 850
 
3.9%
7 800
 
3.7%
8 748
 
3.5%
9 690
 
3.2%
Other values (207) 11628
53.7%
ValueCountFrequency (%)
0 1686
7.8%
1 1184
5.5%
2 1104
5.1%
3 1048
4.8%
4 977
4.5%
5 926
4.3%
6 850
3.9%
7 800
3.7%
8 748
3.5%
9 690
3.2%
ValueCountFrequency (%)
509 1
< 0.1%
490 1
< 0.1%
481 1
< 0.1%
381 1
< 0.1%
363 1
< 0.1%
359 1
< 0.1%
316 1
< 0.1%
311 2
< 0.1%
286 1
< 0.1%
268 1
< 0.1%

Large
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct134
Distinct (%)0.6%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean12.56962
Minimum0
Maximum214
Zeros1937
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size169.2 KiB
2023-07-30T14:20:24.615973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median8
Q317
95-th percentile40
Maximum214
Range214
Interquartile range (IQR)14

Descriptive statistics

Standard deviation14.701477
Coefficient of variation (CV)1.1696039
Kurtosis15.413367
Mean12.56962
Median Absolute Deviation (MAD)6
Skewness2.8985113
Sum271994
Variance216.13342
MonotonicityNot monotonic
2023-07-30T14:20:24.729352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1937
 
9.0%
1 1535
 
7.1%
2 1454
 
6.7%
3 1308
 
6.0%
4 1182
 
5.5%
5 1102
 
5.1%
6 950
 
4.4%
7 897
 
4.1%
8 861
 
4.0%
9 783
 
3.6%
Other values (124) 9630
44.5%
ValueCountFrequency (%)
0 1937
9.0%
1 1535
7.1%
2 1454
6.7%
3 1308
6.0%
4 1182
5.5%
5 1102
5.1%
6 950
4.4%
7 897
4.1%
8 861
4.0%
9 783
3.6%
ValueCountFrequency (%)
214 1
< 0.1%
192 1
< 0.1%
189 1
< 0.1%
182 1
< 0.1%
181 1
< 0.1%
166 1
< 0.1%
157 1
< 0.1%
154 1
< 0.1%
149 1
< 0.1%
148 1
< 0.1%

Mature
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct194
Distinct (%)0.9%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean20.471325
Minimum0
Maximum322
Zeros1477
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size169.2 KiB
2023-07-30T14:20:24.850737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median12
Q327
95-th percentile68
Maximum322
Range322
Interquartile range (IQR)23

Descriptive statistics

Standard deviation24.260359
Coefficient of variation (CV)1.1850898
Kurtosis11.330338
Mean20.471325
Median Absolute Deviation (MAD)9
Skewness2.6493732
Sum442979
Variance588.56502
MonotonicityNot monotonic
2023-07-30T14:20:24.957616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1477
 
6.8%
1 1078
 
5.0%
2 1066
 
4.9%
3 932
 
4.3%
4 907
 
4.2%
5 801
 
3.7%
6 770
 
3.6%
7 749
 
3.5%
8 697
 
3.2%
9 636
 
2.9%
Other values (184) 12526
57.9%
ValueCountFrequency (%)
0 1477
6.8%
1 1078
5.0%
2 1066
4.9%
3 932
4.3%
4 907
4.2%
5 801
3.7%
6 770
3.6%
7 749
3.5%
8 697
3.2%
9 636
2.9%
ValueCountFrequency (%)
322 1
 
< 0.1%
299 1
 
< 0.1%
271 1
 
< 0.1%
265 1
 
< 0.1%
233 1
 
< 0.1%
227 1
 
< 0.1%
226 1
 
< 0.1%
202 3
< 0.1%
197 1
 
< 0.1%
196 1
 
< 0.1%

Region
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.2 KiB
Bas-Sassandra
7842 
Gôh-Djiboua
4763 
Sassandra-Marahoué
3300 
Montagnes
2534 
Lagunes
1407 
Other values (2)
1795 

Length

Max length18
Median length13
Mean length11.781064
Min length4

Characters and Unicode

Total characters254954
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLagunes
2nd rowBas-Sassandra
3rd rowBas-Sassandra
4th rowBas-Sassandra
5th rowBas-Sassandra

Common Values

ValueCountFrequency (%)
Bas-Sassandra 7842
36.2%
Gôh-Djiboua 4763
22.0%
Sassandra-Marahoué 3300
15.2%
Montagnes 2534
 
11.7%
Lagunes 1407
 
6.5%
Comoé 1380
 
6.4%
Lacs 415
 
1.9%

Length

2023-07-30T14:20:25.079945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T14:20:25.208145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
bas-sassandra 7842
36.2%
gôh-djiboua 4763
22.0%
sassandra-marahoué 3300
15.2%
montagnes 2534
 
11.7%
lagunes 1407
 
6.5%
comoé 1380
 
6.4%
lacs 415
 
1.9%

Most occurring characters

ValueCountFrequency (%)
a 56987
22.4%
s 34482
13.5%
n 17617
 
6.9%
- 15905
 
6.2%
r 14442
 
5.7%
o 13357
 
5.2%
S 11142
 
4.4%
d 11142
 
4.4%
u 9470
 
3.7%
h 8063
 
3.2%
Other values (16) 62347
24.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 201503
79.0%
Uppercase Letter 37546
 
14.7%
Dash Punctuation 15905
 
6.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 56987
28.3%
s 34482
17.1%
n 17617
 
8.7%
r 14442
 
7.2%
o 13357
 
6.6%
d 11142
 
5.5%
u 9470
 
4.7%
h 8063
 
4.0%
b 4763
 
2.4%
i 4763
 
2.4%
Other values (8) 26417
13.1%
Uppercase Letter
ValueCountFrequency (%)
S 11142
29.7%
B 7842
20.9%
M 5834
15.5%
D 4763
12.7%
G 4763
12.7%
L 1822
 
4.9%
C 1380
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 15905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 239049
93.8%
Common 15905
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 56987
23.8%
s 34482
14.4%
n 17617
 
7.4%
r 14442
 
6.0%
o 13357
 
5.6%
S 11142
 
4.7%
d 11142
 
4.7%
u 9470
 
4.0%
h 8063
 
3.4%
B 7842
 
3.3%
Other values (15) 54505
22.8%
Common
ValueCountFrequency (%)
- 15905
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 245511
96.3%
None 9443
 
3.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 56987
23.2%
s 34482
14.0%
n 17617
 
7.2%
- 15905
 
6.5%
r 14442
 
5.9%
o 13357
 
5.4%
S 11142
 
4.5%
d 11142
 
4.5%
u 9470
 
3.9%
h 8063
 
3.3%
Other values (14) 52904
21.5%
None
ValueCountFrequency (%)
ô 4763
50.4%
é 4680
49.6%

Date
Date

Distinct251
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size169.2 KiB
Minimum1997-10-01 00:00:00
Maximum2023-07-01 00:00:00
2023-07-30T14:20:25.321384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:25.434766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-07-30T14:20:17.198538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:19:59.510639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:00.823502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:02.132429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:03.476773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:05.013613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:06.338301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:07.630829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:08.941073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:10.300297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:11.872176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:13.189188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:14.532423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:15.882985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:17.295732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:19:59.599932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:00.913700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:02.222978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:03.565871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:05.094845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:06.436975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:07.719949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:09.030519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:10.388300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:11.969236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:13.286422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:14.637548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:15.980133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:17.392944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:19:59.697284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:00.995129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:02.320276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:03.654964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:05.184047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:06.526287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:07.817171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:09.127605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:10.477460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:12.058440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:13.383753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:14.725545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:16.061252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:17.484471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:19:59.794600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:01.092490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:02.409576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:03.752351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:05.290891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:06.623497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:07.916886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:09.223607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:10.850837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:12.155672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:13.474541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:14.815094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:16.150561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:17.581872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:19:59.884009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:01.189682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:02.506993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:03.842374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:05.382505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:06.712711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:08.014147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:09.320768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:10.941725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:12.252917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:13.571776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:14.912362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:16.248716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:17.672464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:19:59.973325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:01.295021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:02.614274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:04.153991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:05.478704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:06.810781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:08.103577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:09.412885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:11.038965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:12.358059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:13.669142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:15.013010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:16.346148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:17.760535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:00.060784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:01.377264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:02.711442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:04.243184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:05.593702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:06.898782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:08.192812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:09.510087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:11.118907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:12.448714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:13.758675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:15.102297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:16.443423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:17.851242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:00.165933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:01.474530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:02.799482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:04.348366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:05.691706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:06.996930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:08.289940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:09.607298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:11.216132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:12.537947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:13.855863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:15.207440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:16.541274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:17.948621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:00.261935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:01.571687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:02.898516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:04.453512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:05.784700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:07.094074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:08.387223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:09.696483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:11.313254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:12.636355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:13.954745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:15.296706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:16.638448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:18.045922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:00.351126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:01.661083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:02.995669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:04.542683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:05.869724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:07.183337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:08.483231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:09.793707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:11.403783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:12.725520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:14.052020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:15.395658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:16.727839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:18.144711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:00.442213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:01.750148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:03.093039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:04.639742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:05.966864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:07.272693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:08.572443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:09.898911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:11.493225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:12.815045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:14.141202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:15.486375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:16.825008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:18.250116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:00.547441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:01.855590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:03.190670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:04.736801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:06.064013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:07.361959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:08.671277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:09.998702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:11.598508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:12.912281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:14.240293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:15.583640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:16.931901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:18.339374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:00.636798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:01.944671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:03.279910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:04.835049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:06.160016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:07.452324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:08.760606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:10.103912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:11.687743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:13.002853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:14.337687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:15.688859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:17.019902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:18.430069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:00.726096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:02.026983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:03.378660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:04.917603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:06.249197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:07.532325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:08.849822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:10.202906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:11.776850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:13.092059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:14.426975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:15.778021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T14:20:17.101331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-30T14:20:25.549442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
WeekYearMonthSiteNumberMoistureTextureCanopyMaintenanceFlowersLeaf flushingTinySmallLargeMatureSiteLetterRegion
Week1.000-0.0690.998-0.0100.3010.2330.0580.022-0.0460.1450.0300.0380.0750.2350.0060.000
Year-0.0691.000-0.0530.5700.078-0.145-0.1030.0010.2410.100-0.0540.0030.1390.0040.1530.141
Month0.998-0.0531.000-0.0020.2940.2320.0560.024-0.0440.1460.0290.0380.0770.2360.0080.000
SiteNumber-0.0100.570-0.0021.0000.052-0.0280.0750.1570.2660.076-0.0140.0090.0780.0280.2790.300
Moisture0.3010.0780.2940.0521.0000.5640.0840.0640.1550.1230.1380.1250.0980.1410.0370.033
Texture0.233-0.1450.232-0.0280.5641.0000.1880.1470.1580.1060.1010.0700.0590.1120.0820.048
Canopy0.058-0.1030.0560.0750.0840.1881.0000.4700.2700.0060.2300.2190.1800.1900.0470.043
Maintenance0.0220.0010.0240.1570.0640.1470.4701.0000.1800.0010.1600.1930.1700.1450.0540.051
Flowers-0.0460.241-0.0440.2660.1550.1580.2700.1801.0000.3210.2440.2090.1320.0590.0550.075
Leaf flushing0.1450.1000.1460.0760.1230.1060.0060.0010.3211.0000.0270.0290.0240.0450.0180.024
Tiny0.030-0.0540.029-0.0140.1380.1010.2300.1600.2440.0271.0000.6750.3140.0870.0270.030
Small0.0380.0030.0380.0090.1250.0700.2190.1930.2090.0290.6751.0000.6170.2810.0170.027
Large0.0750.1390.0770.0780.0980.0590.1800.1700.1320.0240.3140.6171.0000.5910.0170.037
Mature0.2350.0040.2360.0280.1410.1120.1900.1450.0590.0450.0870.2810.5911.0000.0320.030
SiteLetter0.0060.1530.0080.2790.0370.0820.0470.0540.0550.0180.0270.0170.0170.0321.0000.700
Region0.0000.1410.0000.3000.0330.0480.0430.0510.0750.0240.0300.0270.0370.0300.7001.000

Missing values

2023-07-30T14:20:18.567167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-30T14:20:19.110764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-30T14:20:19.305097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

WeekYearMonthSiteLetterSiteNumberMoistureTextureCanopyMaintenanceFlowersLeaf flushingTinySmallLargeMatureRegionDate
044199710AG2NaNNaNNaNNaNNaNNaN2.04.02.096.0Lagunes1997-10-01
144199710SP11NaNNaNNaNNaNNaNNaN1.02.04.089.0Bas-Sassandra1997-10-01
244199710SP4NaNNaNNaNNaNNaNNaN1.03.03.017.0Bas-Sassandra1997-10-01
344199710SP13NaNNaNNaNNaNNaNNaN13.015.012.0124.0Bas-Sassandra1997-10-01
444199710SP14NaNNaNNaNNaNNaNNaN2.00.00.0112.0Bas-Sassandra1997-10-01
544199710SP17NaNNaNNaNNaNNaNNaN42.037.019.061.0Bas-Sassandra1997-10-01
644199710SP23NaNNaNNaNNaNNaNNaN4.02.00.067.0Bas-Sassandra1997-10-01
744199710SP10NaNNaNNaNNaNNaNNaN15.01.01.035.0Bas-Sassandra1997-10-01
844199710SP24NaNNaNNaNNaNNaNNaN2.07.01.01.0Bas-Sassandra1997-10-01
944199710SP27NaNNaNNaNNaNNaNNaN0.01.01.029.0Bas-Sassandra1997-10-01
WeekYearMonthSiteLetterSiteNumberMoistureTextureCanopyMaintenanceFlowersLeaf flushingTinySmallLargeMatureRegionDate
216313020237GA20NaN3.03.03.02.02.021.07.019.016.0Gôh-Djiboua2023-07-01
216323020237FR203NaN3.02.02.02.01.011.041.016.08.0Bas-Sassandra2023-07-01
216333020237FR12NaN3.02.01.02.04.08.011.039.034.0Bas-Sassandra2023-07-01
216343020237FR11NaN3.03.03.02.01.03.014.028.07.0Bas-Sassandra2023-07-01
216353020237DA322NaN4.02.03.04.01.06.030.08.05.0Sassandra-Marahoué2023-07-01
216363020237DA309NaN4.04.02.04.01.018.015.012.06.0Sassandra-Marahoué2023-07-01
216373020237DA409NaN4.03.03.03.02.06.03.06.04.0Sassandra-Marahoué2023-07-01
216383020237DA416NaN4.02.03.03.02.05.03.06.07.0Sassandra-Marahoué2023-07-01
216393020237FR423NaN3.03.03.02.02.070.034.022.014.0Bas-Sassandra2023-07-01
216403020237SP428NaN4.03.04.04.02.022.066.050.042.0Bas-Sassandra2023-07-01